## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
# loading COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
# loading state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
cv_states <- merge(cv_states, state_pops, by="state")Lab 11
Lab Description
We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.
The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this
Steps
I. Reading and processing the New York Times (NYT) state-level COVID-19 data
1. Read in the data
- Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
- Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv””
- Merge datasets
2. Look at the data
- Inspect the dimensions,
head, andtailof the data - Inspect the structure of each variables. Are they in the correct format?
dim(cv_states)[1] 58094 9
head(cv_states) state date fips cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL
2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL
3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL
4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL
5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL
6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL
tail(cv_states) state date fips cases deaths geo_id population pop_density abb
58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY
58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY
58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY
58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY
58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY
58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY
str(cv_states)'data.frame': 58094 obs. of 9 variables:
$ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
$ date : IDate, format: "2023-01-04" "2020-04-25" ...
$ fips : int 1 1 1 1 1 1 1 1 1 1 ...
$ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
$ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
$ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
$ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
$ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
$ abb : chr "AL" "AL" "AL" "AL" ...
The dataset contains 9 variables with appropriate data types: state and abb are character variables, date does not appear to be in the correct date format (IDate), while fips, cases, deaths, geo_id, and population are integers. The pop_density variable is numeric, which is suitable for continuous data. Overall, most variables are in the correct format for analysis.
3. Format the data
- Make date into a date variable
- Make state into a factor variable
- Order the data first by state, second by date
- Confirm the variables are now correctly formatted
- Inspect the range values for each variable. What is the date range? The range of cases and deaths?
# formatting date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# formatting the state and state abbreviation variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
# ordering the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# confirming variable formats
str(cv_states)'data.frame': 58094 obs. of 9 variables:
$ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
$ date : Date, format: "2020-03-13" "2020-03-14" ...
$ fips : int 1 1 1 1 1 1 1 1 1 1 ...
$ cases : int 6 12 23 29 39 51 78 106 131 157 ...
$ deaths : int 0 0 0 0 0 0 0 0 0 0 ...
$ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
$ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
$ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
$ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states) state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
tail(cv_states) state date fips cases deaths geo_id population pop_density abb
57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY
57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY
57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY
57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY
58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY
57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY
# inspecting the range values for each variable.
head(cv_states) state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states) state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)[1] "2020-01-21"
max(cv_states$date)[1] "2023-03-23"
The date range in the dataset spans from January 21, 2020, to March 23, 2023. The range of cases varies from 1 to 12,169,158, while the deaths range from 0 to 104,277. Other variables, such as population and pop_density, also exhibit broad ranges, with population values spanning from 577,737 to 39,557,045 and pop_density ranging from 1.292 to 11,490.12.
4. Add new_cases and new_deaths and correct outliers
- Add variables for new cases,
new_cases, and new deaths,new_deaths:- Hint: You can set
new_casesequal to the difference between cases on date i and date i-1, starting on date i=2
- Hint: You can set
- Use
plotlyfor EDA: See if there are outliers or values that don’t make sense fornew_casesandnew_deaths. Which states and which dates have strange values? - Correct outliers: Set negative values for
new_casesornew_deathsto 0 - Recalculate
casesanddeathsas cumulative sum of updatednew_casesandnew_deaths - Get the rolling average of new cases
# adding variables for new_cases and new_deaths
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
cv_subset = cv_subset[order(cv_subset$date),]
# adding starting level for new cases and deaths
cv_subset$new_cases = cv_subset$cases[1]
cv_subset$new_deaths = cv_subset$deaths[1]
for (j in 2:nrow(cv_subset)) {
cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
}
# including in main dataset
cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")
# inspecting outliers using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)p1<-NULL
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)p2<-NULL
# setting negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# recalculating `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
# add starting level for new cases and deaths
cv_subset$cases = cv_subset$cases[1]
cv_subset$deaths = cv_subset$deaths[1]
for (j in 2:nrow(cv_subset)) {
cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
# smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# inspecting data again
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)******ANS
5. Add additional variables
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (
numeric). You can use the following variable names:per100k= cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k= deaths per 100,000newdeathsper100k= new deaths per 100,000
Add a “naive CFR” variable representing
deaths / caseson each date for each stateCreate a dataframe representing values on the most recent date,
cv_states_today, as done in lecture
# add population normalized (by 100,000) counts for each variable
cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))Warning: NAs introduced by coercion
cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))Warning: NAs introduced by coercion
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))II. Scatterplots
6. Explore scatterplots using plot_ly()
- Create a scatterplot using
plot_ly()representingpop_densityvs. various variables (e.g.cases,per100k,deaths,deathsper100k) for each state on most recent date (cv_states_today)- Size the points by state population
- Use hover to identify any outliers.
- Remove those outliers and replot.
- Choose one plot. For this plot:
- Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
- Add layout information to title the chart and the axes
- Enable
hovermode = "compare"
# pop_density vs. cases
cv_states_today %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# pop_density vs. deathsper100k
cv_states_today_scatter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# Adding hoverinfo
cv_states_today_scatter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
hoverinfo = 'text',
text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") ,
paste(" Deaths per 100k: ",
deathsper100k, sep=""), sep = "<br>")) %>%
layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
hovermode = "compare")Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
- For
pop_densityvs.newdeathsper100kcreate a chart with the same variables usinggglot_ly() - Explore the pattern between \(x\) and \(y\) using
geom_smooth()- Explain what you see. Do you think
pop_densityis a correlate ofnewdeathsper100k?
- Explain what you see. Do you think
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).
Warning: The following aesthetics were dropped during statistical transformation: size.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
While there is a slight upward trend in deathsper100k as pop_density increases, the relationship is not strong or consistent, with a lot of variation across lower density values. This suggests that pop_density may have some association with deathsper100k, but it does not appear to be a strong or direct correlate.
8. Multiple line chart
- Create a line chart of the
naive_CFRfor all states over time usingplot_ly()- Use the zoom and pan tools to inspect the
naive_CFRfor the states that had an increase in September. How have they changed over time?
- Use the zoom and pan tools to inspect the
- Create one more line chart, for Florida only, which shows
new_casesandnew_deathstogether in one plot. Hint: useadd_layer()- Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines") The plot shows a significant spike in new COVID-19 cases around January 10 2022, peaking at approximately 80,000 new cases. The peak for deaths occured at around September 20th, 2021.
9. Heatmaps
Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)In the heat map of new cases, California stands out the most in January 2022, followed by Texas, New York, and Florida. For the heat map of new cases per 100k, most states show similar rates, peaking in January 2022 with a smaller rise around June 2022. In the two-week heat map of new cases per 100k for the latter half of 2021, Alaska stands out in October, while Louisiana and Mississippi stand out in August.
10. Map
- Create a map to visualize the
naive_CFRby state on October 15, 2021 - Compare with a map visualizing the
naive_CFRby state on most recent date - Plot the two maps together using
subplot(). Make sure the shading is for the same range of values (google is your friend for this) - Describe the difference in the pattern of the CFR.
### For specified date
pick.date = "2021-10-15"
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 125
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ",pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
geo = set_map_details
)
fig_pick.date <- fig
#############
### For Today
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
geo = set_map_details
)
fig_Today <- fig
### Plot together
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)In October 2021, Alaska showed the highest case fatality rate (CFR), followed by Montana and surrounding states, indicating a significantly greater proportion of COVID-19 cases leading to fatalities in these regions compared to others. Most other states had relatively low CFRs during this period, highlighting a regional variation in outcomes. Today, the CFR is consistently low across all states, reflecting substantial progress in COVID-19 management which aligns with the widespread availability of vaccines.